Residue Density Segmentation for Monitoring and Optimizing Tillage Practices

TL;DR

This paper introduces a probabilistic deep learning segmentation method to quantify residue coverage in agricultural fields, enhancing the assessment of tillage practices and their carbon sequestration potential.

Contribution

It presents a novel deep learning approach for residue density segmentation, moving beyond simple classification to detailed coverage analysis for better agricultural management.

Findings

Improved accuracy in residue coverage estimation.

Enhanced identification of fields suitable for sustainable practices.

Potential to optimize carbon sequestration strategies.

Abstract

"No-till" and cover cropping are often identified as the leading simple, best management practices for carbon sequestration in agriculture. However, the root of the problem is more complex, with the potential benefits of these approaches depending on numerous factors including a field's soil type(s), topography, and management history. Instead of using computer vision approaches to simply classify a field a still vs. no-till, we instead seek to identify the degree of residue coverage across afield through a probabilistic deep learning segmentation approach to enable more accurate analysis of carbon holding potential and realization. This approach will not only provide more precise insights into currently implemented practices, but also enable a more accurate identification process of fields with the greatest potential for adopting new practices to significantly impact carbon sequestration in agriculture.